Chongzhi Zang

Dory identifies differential spatial patterns in chromatin tracing data by analyzing structural differences between groups, linking changes to gene regulation.

BART-spatial identifies transcriptional regulators from spatial omics data by integrating spatial and pseudotemporal information with TR binding profiles, outperforming existing methods.

GeneKnow synthesizes biological evidence from literature with auditability, achieving higher claim support and citation fidelity than existing AI systems.

The paper shows TEAD1 forms transcriptionally inactive condensates in cancer cell pericentromeric heterochromatin, acting as storage sites and influencing YAP/TEAD target gene expression.

PATTY corrects open chromatin bias in CUT&Tag data using ATAC-seq and machine learning, improving detection of histone modifications and cell clustering in single-cell analyses.

This study identifies CEBPD as a tumor suppressor gene in acute myeloid leukemia through functional experiments and genomic analyses.

BARTsc identifies key transcriptional regulators from single-cell omics data by integrating ChIP-seq profiles and outperforms existing methods in multiple biological systems.

This study uses single-cell multi-omics to show that diabetic mice show persistent aortic smooth muscle cell dysfunction and phenotypic changes despite glycemic control, with partial reversal by dapagliflozin.

This study identifies age-associated delays in immune resolution to respiratory viral infection by analyzing single-cell RNA sequencing and flow cytometry data from young and aged mice.

The paper identifies transcriptional regulators involved in pH-sensitive transcriptional condensates that modulate immune gene expression in macrophages under acidic conditions.

The paper uses super-resolution microscopy to show that estrogen receptor activity regulates distinct chromatin structures marked by H3K27ac, revealing dynamic epigenetic states and their implications for transcriptional activity and cancer resistance.

This study investigates the role of ORC2 in epigenetic regulation and chromosome structure through a multi-omics analysis of a human cell line with ORC mutations.

The paper introduces Perturb-tracing, a high-content screening platform to identify regulators of 3D chromatin organization, discovering new factors that influence chromatin folding and nuclear architecture.

DARDN uses deep learning to classify cancer-specific CTCF binding sites and identify oncogenic transcription factors in multiple cancer types.

This study performs a meta-analysis of scRNA-seq datasets to map human atherosclerosis cell diversity and identifies disease-relevant vascular cell states and markers.

The paper analyzes DNA replication origins and ORC/MCM binding sites in human cells, finding minimal overlap and suggesting origins are specified by stochastic events rather than specific binding sites.

The paper identifies spatially clustered transcription factor binding patterns at super-enhancers and links them to phase-separated transcriptional condensates.

The paper identifies Fob1-dependent condensin recruitment and loop extrusion at the RDT1 locus on yeast chromosome III, linking it to MATa-specific organization during mating-type switching.

The paper introduces TRIPOD, a nonparametric method to detect TF-target gene-accessibility relationships in multiomic data, validated across multiple datasets.

The paper uses single-cell ATAC-seq to analyze coronary artery cells from CAD patients, identifying regulatory mechanisms and risk variants associated with disease.

The paper introduces EPICS, a machine-learning method to analyze 3D chromatin images and identify open/closed chromatin domains with explainable features.

SELMA estimates intrinsic cleavage biases in bulk and single-cell chromatin accessibility data, improving analysis accuracy and cell clustering in single-cell ATAC-seq.

BART3D identifies transcriptional regulators linked to differential chromatin interactions using Hi-C data and ChIP-seq, showing effectiveness in detecting TRs during cellular differentiation or perturbation.

The paper identifies cancer-specific CTCF binding patterns across six cancer types and shows they are linked to oncogenic transcriptional changes without always involving DNA methylation or mutations.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

BARTweb identifies transcription factors associated with genomic features using public ChIP-seq data, outperforming existing tools and reducing computation time.

This study identifies persistent transcriptional and epigenetic changes in human lung epithelial cells after nickel exposure, revealing mechanisms behind long-term health effects.

The paper shows that polyadenylation of H3.1 mRNA displaces H3.3 from gene regulatory elements, leading to cell transformation and genomic instability.

The paper identifies YY1 as a cis-regulator of ERBB2 in MCF10A 3D cultures under high stiffness conditions using integrative transcriptomic and ChIP-seq analysis.

BART predicts functional transcription factors regulating a gene set or associated with a genomic profile using existing ChIP-seq data.